Collaborative Filtering

1. Collaborative Filtering Presented By: Sadaf Baloch MS(SE)

2. Introduction • Collaborative filtering is a method of : • making automatic predictions /recommendations about the interests of a user by collecting taste information from many users • The main idea is: • To automate the process of "word-of-mouth" by which people recommend products or services to one another.

3. Why Collaborative filtering? • If you need to choose between a variety of options with which you do not have any experience, you will often rely on the opinions of others who do have such experience. • Instead of asking opinions to each individual, you might try to determine an average opinion for the group. • However, ignores your particular interests, which may be different from those of the average person.

4. Who those “many users” are? • Who share the same rating patterns with the active user. • These ratings can be used to calculate prediction for the active user.

5. Mechanism • A large group of people's preferences are taken in account • A subgroup of people is selected whose preferences are similar to the preferences of the person who seeks advice • Average of the preferences for that subgroup is calculated • The resulting preference function is used to recommend options on which the advice-seeker has expressed no personal opinion as yet.

6. Bottleneck • Collection of Preferences • To be reliable, the system needs a very large number of people to express their preferences about a relatively large number of options . • This requires quite a lot of effort from a lot of people. Since the system only becomes useful after a "critical mass" of opinions has been collected. • People will not be very motivated to express detailed preferences in the beginning stages

7. The End ..

8. References • http://pespmc1.vub.ac.be/collfilt.html